Health expenditure and gross domestic product: causality analysis by income level

  • Rezwanul Hasan RanaEmail author
  • Khorshed Alam
  • Jeff Gow
Research article


The empirical findings on the relationship between gross domestic product (GDP) and health expenditure are diverse. The influence of income levels on this causal relationship is unclear. This study examines if the direction of causality and income elasticity of health expenditure varies with income level. It uses the 1995–2014 panel data of 161 countries divided into four income groups. Unit root, cointegration and causality tests were employed to examine the relationship between GDP and health expenditure. Impulse-response functions and forecast-error variance decomposition tests were conducted to measure the responsiveness of health expenditure to changes in GDP. Finally, the common correlated effects mean group method was used to examine the income elasticity of health expenditure. Findings show that no long-term cointegration exists, and the growth in health expenditure and GDP across income levels has a different causal relationship when cross-sectional dependence in the panel is accounted for. About 43% of the variation in global health expenditure growth can be explained by economic growth. Income shocks affect health expenditure of high-income countries more than lower-income countries. Lastly, the income elasticity of health expenditure is less than one for all income levels. Therefore, healthcare is a necessity. In comparison with markets, governments have greater obligation to provide essential health care services. Such results have noticeable policy implications, especially for low-income countries where GDP growth does not cause increased health expenditure.


Health expenditure Gross domestic product Westerlund cointergration Causality analysis Impulse response function Common correlated effects 

JEL Classification

C55 I10 I15 O1 



The paper was part of the first author’s Ph.D. study. The Ph.D. program was financed by the University of Southern Queensland, Australia [USQ International Stipend Research Scholarship and USQ International Fees Research Scholarship].

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.


  1. Abrigo, M. R., & Love, I. (2016). Estimation of panel vector autoregression in Stata. Stata Journal,16, 778–804.CrossRefGoogle Scholar
  2. Acemoglu, D., & Johnson, S. (2007). Disease and development: The effect of life expectancy on economic growth. Journal of Political Economy,115(6), 925–985.CrossRefGoogle Scholar
  3. Adriana, D. (2014). Revisiting the relationship between unemployment rates and shadow economy. A Toda–Yamamoto approach for the case of Romania. Procedia Economics and Finance,10, 227–236.CrossRefGoogle Scholar
  4. Amiri, A., & Ventelou, B. (2012). Granger causality between total expenditure on health and GDP in OECD: Evidence from the Toda–Yamamoto approach. Economics Letters,116(3), 541–544. Scholar
  5. Asteriou, D. (2009). Foreign aid and economic growth: New evidence from a panel data approach for five South Asian countries. Journal of Policy Modeling,31(1), 155–161.CrossRefGoogle Scholar
  6. Atilgan, E., Kilic, D., & Ertugrul, H. M. (2016). The dynamic relationship between health expenditure and economic growth: Is the health-led growth hypothesis valid for Turkey? European Journal of Health Economics,18(5), 567–574. Scholar
  7. Baltagi, B. H., Lagravinese, R., Moscone, F., & Tosetti, E. (2017). Health care expenditure and income: A global perspective. Health Economics,26(7), 863–874.PubMedCrossRefGoogle Scholar
  8. Baltagi, B. H., & Moscone, F. (2010). Health care expenditure and income in the OECD reconsidered: Evidence from panel data. Economic Modelling,27(4), 804–811. Scholar
  9. Bloom, D. E., Canning, D., & Sevilla, J. (2004). The effect of health on economic growth: A production function approach. World Development,32(1), 1–13.CrossRefGoogle Scholar
  10. Carrion-i-Silvestre, J. L. (2005). Health care expenditure and GDP: Are they broken stationary? Journal of Health Economics,24(5), 839–854. Scholar
  11. Chen, W., Clarke, J. A., & Roy, N. (2013). Health and wealth: Short panel Granger causality tests for developing countries. The Journal of International Trade and Economic Development,23(6), 755–784. Scholar
  12. Chudik, A., Pesaran, M. H., & Tosetti, E. (2011). Weak and strong cross‐section dependence and estimation of large panels. The Econometrics Journal, 14(1), C45–C90. Scholar
  13. Clarke, J. A., & Mirza, S. (2006). A comparison of some common methods for detecting Granger noncausality. Journal of Statistical Computation and Simulation,76(3), 207–231.CrossRefGoogle Scholar
  14. Clemente, J., Marcuello, C., Montañés, A., & Pueyo, F. (2004). On the international stability of health care expenditure functions: Are government and private functions similar? Journal of Health Economics,23(3), 589–613.PubMedCrossRefGoogle Scholar
  15. Dolado, J. J., & Lütkepohl, H. (1996). Making Wald tests work for cointegrated VAR systems. Econometric Reviews,15(4), 369–386.CrossRefGoogle Scholar
  16. Dumitrescu, E. I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling,29(4), 1450–1460.CrossRefGoogle Scholar
  17. Erdil, E., & Yetkiner, I. H. (2009). The Granger-causality between health care expenditure and output: A panel data approach. Applied Economics,41(4), 511–518. Scholar
  18. Everaert, G., & De Groote, T. (2016). Common correlated effects estimation of dynamic panels with cross-sectional dependence. Econometric Reviews,35(3), 428–463.CrossRefGoogle Scholar
  19. Farag, M., Nandakumar, A., Wallack, S., Hodgkin, D., Gaumer, G., & Erbil, C. (2013). Health expenditures, health outcomes and the role of good governance. International Journal of Health Care Finance and Economics,13(1), 33–52.PubMedCrossRefGoogle Scholar
  20. Gengenbach, C., Palm, F. C., & Urbain, J. P. (2006). Cointegration testing in panels with common factors. Oxford Bulletin of Economics and Statistics,68(1), 683–719.CrossRefGoogle Scholar
  21. Glied, S., & Smith, P. C. (2011). The Oxford handbook of health economics. Oxford: Oxford University Press.CrossRefGoogle Scholar
  22. Granados, J. A. T. (2012). Economic growth and health progress in England and Wales: 160 years of a changing relation. Social Science and Medicine,74(5), 688–695.CrossRefGoogle Scholar
  23. Halici-Tuluce, N. S., Dogan, I., & Dumrul, C. (2016). Is income relevant for health expenditure and economic growth nexus? International Journal of Health Economics and Management,16(1), 23–49. Scholar
  24. Hall, S. G., & Jones, C. I. (2007). The value of life and the rise in health spending. The Quarterly Journal of Economics,122(2007), 39–72.CrossRefGoogle Scholar
  25. Hall, S. G., Swamy, P. A. V. B., & Tavlas, G. S. (2011). Generalized cointegration: A new concept with an application to health expenditure and health outcomes. Empirical Economics,42(2), 603–618. Scholar
  26. Hansen, P., & King, A. (1996). The determinants of health care expenditure: A cointegration approach. Journal of Health Economics,15, 127–137.PubMedCrossRefGoogle Scholar
  27. Harris, R. D., & Tzavalis, E. (1999). Inference for unit roots in dynamic panels where the time dimension is fixed. Journal of Econometrics,91(2), 201–226.CrossRefGoogle Scholar
  28. Hartwig, J. (2008). What drives health care expenditure? Baumol’s model of ‘Unbalanced growth’ revisited. Journal of Health Economics,27, 603–623.PubMedCrossRefGoogle Scholar
  29. Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics,115(1), 53–74.CrossRefGoogle Scholar
  30. Kapetanios, G., Pesaran, M. H., & Yamagata, T. (2011). Panels with non-stationary multifactor error structures. Journal of Econometrics,160(2), 326–348.CrossRefGoogle Scholar
  31. Ke, X., Saksena, P., & Holly, A. (2011). The determinants of health expenditure: A country-level panel data analysis. Working paper of the Results for Development Institute (R4D). Geneva: World Health Organization. Accessed on August 11th, 2017.
  32. Koop, G., Pesaran, M. H., & Potter, S. M. (1996). Impulse response analysis in nonlinear multivariate models. Journal of Econometrics,74(1), 119–147.CrossRefGoogle Scholar
  33. Lago-Peñas, S., Cantarero-Prieto, D., & Blázquez-Fernández, C. (2013). On the relationship between GDP and health care expenditure: A new look. Economic Modelling,32, 124–129. Scholar
  34. Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: Asymptotic and finite-sample properties. Journal of Econometrics,108(1), 1–24.CrossRefGoogle Scholar
  35. Liddle, B., & Messinis, G. (2015). Which comes first-urbanization or economic growth? Evidence from heterogeneous panel causality tests. Applied Economics Letters,22(5), 349–355.CrossRefGoogle Scholar
  36. Lütkepohl, H., & Krätzig, M. (2004). Applied time series econometrics. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  37. MacDonald, G., & Hopkins, S. (2002). Unit root properties of OECD health care expenditure and GDP data. Health Economics,11(4), 371–376. Scholar
  38. McCoskey, S. K., & Selden, T. M. (1998). Health care expenditures and GDP: Panel data unit root test results. Journal of Health Economics,17, 369–376.PubMedCrossRefGoogle Scholar
  39. Menard, A.-R., & Weill, L. (2016). Understanding the link between aid and corruption: A causality analysis. Economic Systems,40(2), 260–272.CrossRefGoogle Scholar
  40. Mladenović, I., Milovančević, M., Sokolov Mladenović, S., Marjanović, V., & Petković, B. (2016). Analyzing and management of health care expenditure and gross domestic product (GDP) growth rate by adaptive neuro-fuzzy technique. Computers in Human Behavior,64, 524–530. Scholar
  41. Moscone, F., & Tosetti, E. (2010). Health expenditure and income in the United States. Health Economics,19(12), 1385–1403. Scholar
  42. Okunade, A. A., & Karakus, M. C. (2001). Unit root and cointegration tests: Timeseries versus panel estimates for international health expenditure models. Applied Economics,33(9), 1131–1137. Scholar
  43. Panopoulou, E., & Pantelidis, T. (2012). Convergence in per capita health expenditures and health outcomes in the OECD countries. Applied Economics,44(30), 3909–3920. Scholar
  44. Persyn, D., & Westerlund, J. (2008). Error-correction-based cointegration tests for panel data. Stata Journal,8(2), 232–241.CrossRefGoogle Scholar
  45. Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. CESifo GmbH, CESifo working paper series: CESifo Working Paper No. 1229. Accessed January 14th, 2018.
  46. Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica,74(4), 967–1012.CrossRefGoogle Scholar
  47. Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics,22(2), 265–312.CrossRefGoogle Scholar
  48. Pesaran, M. H., & Tosetti, E. (2011). Large panels with common factors and spatial correlation. Journal of Econometrics,161(2), 182–202.CrossRefGoogle Scholar
  49. Rafiq, S., Salim, R., & Bloch, H. (2009). Impact of crude oil price volatility on economic activities: An empirical investigation in the Thai economy. Resources Policy,34(3), 121–132.CrossRefGoogle Scholar
  50. Self, S., & Grabowski, R. (2003). How effective is public health expenditure in improving overall health? A cross-country analysis. Applied Economics,35(7), 835–845. Scholar
  51. Shahbaz, M. (2012). Does trade openness affect long run growth? Cointegration, causality and forecast error variance decomposition tests for Pakistan. Economic Modelling,29(6), 2325–2339.CrossRefGoogle Scholar
  52. Shaw, J. W., Horrace, W. C., & Voge, R. J. (2005). The determinants of life expectancy: An analysis of the OECD health data. Southern Economic Journal,71(4), 768–783.CrossRefGoogle Scholar
  53. Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 48(1), 1–48.CrossRefGoogle Scholar
  54. Swanson, N. R., & Granger, C. W. (1997). Impulse response functions based on a causal approach to residual orthogonalization in vector autoregressions. Journal of the American Statistical Association,92(437), 357–367.CrossRefGoogle Scholar
  55. Tamakoshi, T., & Hamori, S. (2015). Testing cointegration between health care expenditure and GDP in Japan with the presence of a regime shift. Applied Economics Letters,23(2), 151–155. Scholar
  56. Toda, H. Y., & Yamamoto, T. (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics,66(1), 225–250.CrossRefGoogle Scholar
  57. van der Gaag, J., & Stimac, V. (2008). Towards a new paradigm for health sector development. Amsterdam: Amsterdam Institute for International Development. Accessed on November 7th, 2017.
  58. Villaverde, J., Maza, A., & Hierro, M. (2014). Health care expenditure disparities in the European Union and underlying factors: A distribution dynamics approach. International Journal of Health Care Finance and Economics,14(3), 251–268.PubMedCrossRefGoogle Scholar
  59. Wang, K. M. (2011). Health care expenditure and economic growth: Quantile panel-type analysis. Economic Modelling,28(4), 1536–1549. Scholar
  60. Wang, Z. (2009). The determinants of health expenditures: Evidence from US state-level data. Applied Economics,41(4), 429–435. Scholar
  61. Wang, Z., & Rettenmaier, A. J. (2007). A note on cointegration of health expenditures and income. Health Economics,16(6), 559–578. Scholar
  62. Westerlund, J. (2007). Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics,69(6), 709–748.CrossRefGoogle Scholar
  63. Wooldridge, J. (2002). Econometric analysis of cross section and panel data. Cambridge: MA: MIT Press.Google Scholar
  64. World Bank. (2016). World development indicators. Accessed on May 21st, 2017.
  65. World Health Organization. (2016). Global health observatory data repository. Accessed on May 21st, 2017.

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of CommerceUniversity of Southern QueenslandToowoombaAustralia
  2. 2.School of Accounting, Economics and FinanceUniversity of KwaZulu-NatalDurbanSouth Africa
  3. 3.School of CommerceUniversity of Southern QueenslandToowoombaAustralia

Personalised recommendations